Multi-Agent Goal Negotiation and the Economy of Meaning

Community Article Published December 28, 2025

Extending GCS and PLB to Markets, Deals, and Distributed Learning Draft v0.1 — Non-normative supplement to SI-Core / SI-NOS / PLB

This document is non-normative. It explores how multi-agent SI systems could negotiate, “trade” goals, and effectively put prices on meaning, using Goal Contribution Scores (GCS) and the Pattern-Learning-Bridge (PLB) as building blocks.

1. Why multi-agent GCS and “semantic markets” exist at all

Up to now, most SI-Core discussions have looked like this:

One core (L2/L3),
One primary goal set,
Possibly many subsystems, but all under a single “city orchestrator” or similar umbrella.

Reality rarely works that way.

A city has multiple owners of goals: transport, grid, hospitals, regulators, citizens.
A cloud platform has multiple tenants and services, each with competing priorities.
A cross-organizational SI network spans different companies, jurisdictions, and ethics regimes.

In SI terms:

Each agent has its own goal vector and its own GCS definitions.
They still share a world, and many actions are joint: one floodgate move affects traffic, hospitals, power, and budget.

If you want SI-Core to govern civilization-scale structures, you eventually need:

A way for agents to negotiate GCS trade-offs:
- “I’ll take a small hit on my goal if you compensate me on yours.”
A way to settle those trades:
- “What is fair compensation? Who owes what to whom?”
A way to price information itself:
- “If I share my semantic state with you, how much does that improve your goals, and how much should it ‘cost’?”

This document sketches how to extend:

GCS → from “per-agent decision metric” to “multi-agent accounting surface”.
PLB → from “single-core learning layer” to “market + negotiation + learning layer”.

Think of it as:

PLB-M — Pattern-Learning-Bridge for Multi-agent / Markets

2. Single-agent recap: GCS and PLB in one core

Brief recap of the pieces we’re building on.

2.1 GCS: Goal Contribution Score (single agent)

For a single agent with a goal (g), GCS is:

GCS_g(a_t) = normalize(
  E[M_g | baseline] - E[M_g | action = a_t]
)

Note: Assume M_g is a loss-style metric (lower is better). If a goal uses a maximize-style metric, apply a direction convention (e.g., convert to loss or multiply by dir_g ∈ {+1,-1}) so that higher GCS is always better.

Where:

(M_g) is a goal metric (e.g., expected flood damage).
“baseline” is typically status quo or a reference policy.
“action” is the candidate decision.

Utility:

Scheduling – pick which actions to consider.
Learning – update policies based on contribution.
Governance – log decisions in a structured way.

For multiple goals (g_1,…,g_k):

GCS(a_t) = (GCS_g1(a_t), ..., GCS_gk(a_t))

And a policy decides how to interpret that vector (lexicographic, constraints, etc.).

2.2 PLB: Pattern-Learning-Bridge (single agent)

PLB watches:

Jump logs,
EthicsTraces,
GCS outcomes,
Rollback events.

And proposes structural patches:

new Saga compensators,
updated ethics policies,
tweaks to GCS estimators,
changes to SIL code.

Everything is:

sandboxed,
validated,
and human-reviewed.

PLB is offline / nearline, never on the jump hot path.

3. Multi-agent world: many goals, shared actions

Now upgrade the picture.

Imagine a city cluster with distinct SI-governed agents:

FloodCore — minimizes city.flood_risk and city.expected_damage.
TrafficCore — minimizes city.travel_delay and fairness gaps.
HealthCore — maximizes hospital.accessibility and patient_outcomes.
GridCore — stabilizes grid.frequency and minimizes outage_risk.

Each has:

its own goal vector (G^A, G^B,…),
its own GCS mapping (GCS^A, GCS^B,…),
possibly its own ethics overlay and policy set.

Yet many actions are coupled:

“Close canal segment 12 by 30cm”
- helps FloodCore,
- harms TrafficCore (more congestion),
- affects HealthCore (ambulances),
- touches GridCore indirectly (if pumps draw more power).

A single joint action (a) yields:

GCS^Flood(a)   = ( +0.80, +0.10 )  # flood risk ↓, damage ↓ a bit
GCS^Traffic(a) = ( -0.35, -0.05 )  # delay ↑, fairness slightly worse
GCS^Health(a)  = ( +0.10, -0.02 )  # access mixed
GCS^Grid(a)    = ( +0.05 )         # risk ↓ a bit

If each core optimizes only its own GCS in isolation, you get:

deadlocks (“everyone vetoes everyone”),
or selfish behavior (one core dominates).

You need a negotiation / settlement layer.

4. Goal-space “plea bargains”: judicial trades between agents

We’ll use a deliberately loaded metaphor: judicial trade (plea bargain) in goal-space.

“I, agent A, accept a small penalty on my goal if agent B compensates me now or later.”

4.1 GCS deltas as negotiable quantities

In this document, assume each agent’s GCS is already defined relative to its baseline (e.g., status quo or a reference policy).

So for a candidate joint action (a), we simply treat the reported per-agent vectors as the negotiable quantities:

ΔGCS^i(a) := GCS^i(a)     # baseline-relative by definition

When discussing one candidate action (a), we can collect:

ΔGCS(a) = {
  Flood:   (+0.80, +0.10),
  Traffic: (-0.35, -0.05),
  Health:  (+0.10, -0.02),
  Grid:    (+0.05)
}

Note (non-normative): if an implementation defines GCS as an absolute metric instead, then set ΔGCS^i(a) = GCS^i(a) - GCS^i(baseline) explicitly — but don’t mix the two conventions.

Interpretation:

FloodCore gains a lot on safety, tiny loss on something else.
TrafficCore loses on delay and fairness.
HealthCore and GridCore see modest improvements.

4.2 Naïve rule: veto or majority

Simple options (which usually fail):

Unanimous veto — if any core’s critical goal is below a floor, reject.
Weighted majority — assign static weights to cores, sum up.

Problems:

Doesn’t let TrafficCore say:

“I’ll accept more congestion if you give me priority later.”
Doesn’t capture dynamic deals or history.

We want negotiable structures.

4.3 A structured “plea bargain” schema

Define a Goal Trade Proposal (GTP):

goal_trade_proposal:
  id: "GTP-2028-04-01T05:00:03.450Z"
  action_ref: "A-FLOOD-ADJUST-12"
  participants: ["FloodCore", "TrafficCore", "HealthCore", "GridCore"]

  terms:
    FloodCore:
      accepts:
        # Bounds are baseline-relative GCS deltas.
        # "min" means: do not go below this value.
        gcs_bounds:
          city.flood_risk_minimization: { min: +0.60 }
      offers:
        commitments:
          - to: "TrafficCore"
            scenario: "post_flood_recovery"
            policy_ref: "RECOVERY-TRAFFIC-001"
            expiry: "2028-04-30"

    TrafficCore:
      accepts:
        gcs_bounds:
          city.travel_delay_minimization: { min: -0.40 }  # tolerate up to this loss
      requires:
        commitments:
          - from: "FloodCore"
            scenario: "post_flood_recovery"
            policy_ref: "RECOVERY-TRAFFIC-001"
            min_prob: 0.95
            expiry: "2028-04-30"

This is illustrative, not normative, but it shows the idea:

Each agent states:
- what GCS hit it is willing to take now;
- what compensation it expects (now or later).
The “deal” is logged and enforced by SI-Core (or a higher-level governance layer).

In practice, you’d keep deals:

simple and bounded (no unbounded future promises),
checked by [ETH] and [EVAL],
settled in a ledger (who owes what to whom).

5. Semantic pricing: putting a “price” on meaning

So far, we traded goal outcomes (GCS deltas). Now we consider:

What is the value of information itself?

5.1 Semantic units as economic goods

From the Semantic Compression doc, a semantic unit looks like:

{
  "type": "city.flood_risk_state/v1",
  "scope": {"sector": 12, "horizon_min": 60},
  "payload": {"risk_score": 0.73, "expected_damage_eur": 1.9e6},
  "confidence": 0.87,
  "backing_refs": ["sim://..."],
  "goals": ["city.flood_risk_minimization", "city.hospital_access"]
}

For a different agent (say, HealthCore), this semantic unit might:

improve its GCS on hospital.accessibility (better ambulance routing),
reduce its need for raw sensor access,
but cost bandwidth / storage / processing.

We can define the semantic value of a unit (u) to agent (i) as an expected improvement in the agent’s baseline-relative GCS outcomes due to having u available:

SV^i(u) := E_a[ GCS^i(a) | with u ] - E_a[ GCS^i(a) | without u ]

Where E_a is taken over actions sampled from agent i's current candidate generator / policy-induced distribution (implementation-defined, but must be consistent within a deployment).

Intuition: “How much better do my chosen actions score on my goals if I can see u?”

If there are multiple goals per agent, SV^i(u) is a vector in the same goal space. A policy may map that vector into either:

PriceVec^i(u) = f_vec( SV^i(u), risk_profile, policy )   # per-goal pricing/weights
PriceScalar^i(u) = f_scalar( SV^i(u), risk_profile, policy ) # single credit cost

Both are accounting mechanisms; they do not need to be money.

Roughly:

“How much better can I do on my goals if I see this semantic unit?”

5.2 Semantic price vectors

If there are multiple goals per agent, we might define a semantic price vector:

Price^i(u) = f( SV^i(u), risk_profile, policy )

Where f is policy-chosen (e.g., prioritizing safety over efficiency).

For agents sharing a network / infrastructure, we can imagine:

a quota of semantic units they can send/receive;
a budget in some abstract unit (credits, energy, etc.);
a clearing mechanism that decides:
- which semantic units get sent,
- which get dropped or delayed,
- who “pays” whom (or what) for the privilege.

None of this needs to be money. It can be purely accounting:

“You consumed X units of ‘flood risk semantic bandwidth’ from the shared infrastructure; in exchange, you owe Y units of support / slack / priority later.”

5.3 Information as a shared resource

Key point:

SI-Core already enforces structural constraints.
Semantic compression already decides what to keep.
Semantic pricing is just:
- “who gets priority when there is conflict?”
- “how do we log and settle those priorities?”

The PLB-M can watch how semantic units flow:

which agents habitually “receive more than they give”,
which semantic types are bottlenecks,
where fairness issues arise.

Then propose:

new quota schemes,
revised semantic routing rules,
adjusted semantic “prices” per goal / per agent.

6. PLB-M: Pattern-Learning-Bridge as market + negotiation + learning

We now have three interacting layers:

Single agents computing GCS and making local decisions.
A multi-agent negotiation layer that trades GCS deltas and semantic bandwidth.
A PLB-M that learns patterns in both:
- joint behavior (GCS trades, deals),
- and semantic flows (who sends/receives what).

PLB-M still lives:

offline / nearline,
behind sandbox + conformance kit,
under [ETH]/[EVAL]/[MEM] governance.

But now its input is richer.

6.1 What PLB-M observes

PLB-M sees:

Joint decision logs:
- joint action a,
- per-agent GCS vectors,
- deal/contract references (GTPs),
- rollback events (who had to pay for what).
Semantic flow logs:
- semantic units u,
- which agents produced/consumed them,
- resulting GCS changes per agent.
Metrics:
- per-agent fairness,
- stability of “prices” over time,
- incidents where deals went badly.

6.2 What PLB-M proposes

PLB-M can propose:

Goal trade patterns:
- Standard “deal templates” between recurring agent pairs.
- e.g., city-wide “flood vs traffic” trade pattern for storms.
Semantic pricing updates:
- Adjust quotas / weights / priorities for semantic units.
- Suggest new classes of semantic units with higher value.
Negotiation protocol tweaks:
- e.g., “require explicit fairness constraints in all GTPs involving region X.”
Meta-governance changes:
- e.g., “limit how much any one agent can accumulate in ‘credit’ without paying it back.”

Each proposal:

is structural (YAML / SIL / config),
goes through sandbox, golden-diff, conformance,
is subject to human review and ethics gating.

7. A simple three-agent negotiation example

Let’s ground all this in a small, concrete example.

7.1 Setup

Three agents:

FloodCore (F)
TrafficCore (T)
HealthCore (H)

Candidate joint action (a): “Partially close gate 12, re-route traffic through sectors 8 and 9.”

The per-agent GCS deltas vs baseline are:

ΔGCS_Flood(a):
  flood_risk_minimization: +0.70
  damage_minimization:     +0.20

ΔGCS_Traffic(a):
  travel_delay_minimization: -0.30
  fairness_index:            -0.05

ΔGCS_Health(a):
  hospital_access: +0.10
  emergency_response_time: +0.05

Interpretation:

F gains a lot (good).
H gains a bit (good).
T loses moderately on delay, slightly on fairness.

7.2 Naïve decision

Without negotiation, a global orchestrator might:

treat flood_risk as lexicographically dominant → always accept,
but this hides the recurring cost to T.

7.3 Structured GTP for this decision

Instead, the negotiation layer constructs a Goal Trade Proposal:

goal_trade_proposal:
  id: "GTP-2028-04-01T05:00:03.900Z"
  action_ref: "A-GATE12-ROUTE89"
  participants: ["FloodCore", "TrafficCore", "HealthCore"]

  terms:
    FloodCore:
      accepts:
        gcs_bounds:
          flood_risk_minimization: { min: +0.60 }
      offers:
        commitments:
          - to: "TrafficCore"
            scenario: "post_flood_recovery"
            policy_ref: "RECOVERY-TRAFFIC-001"
            expiry: "2028-04-30"

    TrafficCore:
      accepts:
        gcs_bounds:
          travel_delay_minimization: { min: -0.35 }
      requires:
        commitments:
          - from: "FloodCore"
            scenario: "post_flood_recovery"
            policy_ref: "RECOVERY-TRAFFIC-001"
            min_prob: 0.95
            expiry: "2028-04-30"

    HealthCore:
      accepts:
        gcs_bounds:
          hospital_access: { min: +0.00 }  # “neutral” expressed as a bound

The settlement logic then:

checks that all hard floors (safety/ethics) are respected (via [ETH]/[EVAL]),
logs this trade as a contract,
updates an internal “obligation ledger” (FloodCore owes TrafficCore some recovery support later).

Later, during recovery, a joint action (b) might favor T (shorter delays) while keeping F above its safety floors, effectively paying back part of this debt.

PLB-M will see the pattern:

repeated “Flood wins now, Traffic compensated later,”
and can suggest standardizing this into a clearer, simpler, and more traceable pattern.

8. Market-style clearing of joint actions

We can formalize multi-agent negotiation as a clearing problem:

Given a set of candidates (A = {a_1,..,a_n}) and agents (i=1..k), each with (ΔGCS^i(a_j)) and deal constraints, choose a subset of actions + trades that satisfies:

safety floors for all agents,

fairness constraints,

some notion of global optimality (policy-dependent).

8.1 Clearing algorithm sketch (non-normative)

Non-normative pseudocode:

def clear_joint_actions(candidates, agents, constraints):
    feasible = []

    for a in candidates:
        deltas = {i: gcs_delta(i, a) for i in agents}

        # 1. Check hard floors (safety/ethics)
        if not all(respects_floors(i, deltas[i]) for i in agents):
            continue

        # 2. Check if a feasible GTP can be formed
        gtp = propose_gtp(a, agents, deltas, constraints)
        if gtp is None:
            continue

        # 3. Compute a clearing score (policy-dependent)
        score = clearing_score(a, deltas, gtp, constraints)
        feasible.append((score, a, gtp))

    # 4. Choose best feasible actions (and trades)
    feasible.sort(reverse=True, key=lambda x: x[0])
    chosen = select_action_set(feasible, constraints)

    return chosen

Where:

propose_gtp() tries to assemble a Goal Trade Proposal that:
- respects each agent’s accepts/requires clauses,
- doesn’t violate ethics,
- fits fairness constraints.
clearing_score() might consider:
- sum of weighted GCS across agents,
- penalties for unfairness or over-centralization,
- budget usage for semantic bandwidth.

PLB-M doesn’t sit in this loop. It watches the outcomes:

Which actions are consistently chosen?
Which agents are consistently “losers” or “winners”?
Where do deals repeatedly fail?

And proposes structural tweaks to:

negotiation policies,
clearing scores,
or even the allowed GTP forms.

8.2 Game-theoretic considerations (non-normative)

This document treats the multi-agent clearing layer in engineering terms, but it clearly has a game-theoretic side:

Nash equilibrium — a state where no agent can unilaterally deviate and improve its own goal vector, given others’ strategies.
Pareto efficiency — a state where no agent can be made better off (in its goals) without making another agent worse off.
Incentive compatibility — whether agents are incentivized to report their true preferences / GCS effects.
Strategy-proofness — whether truthful reporting is a dominant strategy.

For the Goal Trade Proposal (GTP) mechanism sketched here, we do not claim any strong game-theoretic guarantees. Instead, we highlight a few design questions for future work:

Under what conditions do simple GTP rules admit Nash equilibria that are compatible with ethics and safety?
When does a clearing algorithm produce Pareto-efficient outcomes across agents’ goal vectors, as opposed to leaving obvious mutually beneficial trades on the table?
How vulnerable is the mechanism to manipulated reports of GCS deltas, and what kinds of penalties or audits are needed to make truthful reporting approximately incentive compatible?

As a reference point, classic mechanisms such as Vickrey–Clarke–Groves (VCG) payments show how one can design auctions where truthful reporting is a dominant strategy. One possible research direction is to explore VCG-like structures in the space of GTPs and semantic pricing — for example, treating “semantic bandwidth” or “goal slack” as allocatable resources.

A full game-theoretic treatment of multi-agent GCS and GTP is out of scope for this supplement. The intent here is simply to:

make the game-theoretic angle explicit, and
flag desirable properties (stability, fairness, incentive compatibility) that future work should aim for.

8.3 Computational complexity (non-normative)

The clearing problem described above has non-trivial computational cost.

For $n$ candidate actions and $k$ agents:

computing per-agent GCS deltas is typically $O(n \cdot k)$,
constructing candidate GTPs may require checking pairwise and higher-order constraints, which can approach $O(n \cdot k^2)$,
satisfying all constraints together (safety, fairness, quotas) can be equivalent to a constrained combinatorial optimization problem, which is NP-hard in the general case.

In the worst case, trying to find an exactly optimal set of actions and trades could require exponential time.

Practical systems therefore rely on heuristics and approximations, such as:

Greedy heuristics

Rank candidates by a policy-dependent score and accept them one by one as long as constraints remain satisfied.
Constraint propagation / pruning

Use constraints to prune infeasible actions early, before expensive scoring or negotiation, especially for high-risk domains.
Hierarchical clearing

Clear actions in tiers:
- first handle safety-critical decisions with tight constraints,
- then handle lower-stakes decisions with simpler rules.
Time limits

Impose execution time limits and accept “good enough” solutions when the search cannot converge to a provably optimal set in time.
Caching and reuse

Cache the outcomes of clearing for recurring situations (similar context, similar agents) and reuse those results as templates.

For an L3-scale deployment, a typical configuration might aim for:

tens of candidates and a handful of agents per clearing cycle,
latency budgets on the order of 100–500 ms for non-emergency decisions,
more relaxed budgets (seconds) for low-frequency, high-impact planning.

These numbers are illustrative only. The key point is that the clearing layer should be designed:

with explicit complexity bounds in mind, and
with fallbacks for when exact optimization is infeasible.

8.4 Implementation sketch (non-normative)

The following sketch shows how a multi-agent negotiator might be structured in practice. This is illustrative pseudocode, not a normative API.

class MultiAgentNegotiator:
    def propose_gtp(self, action, agents, deltas):
        """
        action: candidate joint action
        agents: list of agent objects
        deltas: mapping agent -> ΔGCS vector for this action
        """
        gtp = GoalTradeProposal(action_ref=action.id)

        # 1. Collect per-agent terms
        for agent in agents:
            terms = agent.negotiate_terms(action, deltas[agent])
            if not terms:
                # This agent refuses any trade for this action
                return None
            gtp.add_terms(agent.id, terms)

        # 2. Validate internal consistency and constraints
        if not self.validate_constraints(gtp):
            return None

        # 3. Ethics / safety checks
        if not self.ethics_check(gtp):
            return None

        return gtp

    def clearing_score(self, action, agents, deltas, gtp):
        """
        Compute a policy-dependent score used by the clearing algorithm.
        """

        # Aggregate contribution across agents with policy weights
        base_score = 0.0
        for agent in agents:
            w = self.agent_weight(agent)
            base_score += w * self.sum_gcs_components(deltas[agent])

        # Penalize unfairness (e.g., same agent losing repeatedly)
        fairness = self.compute_fairness(deltas)
        base_score -= self.fairness_penalty(fairness)

        # Account for trade complexity / overhead
        trade_cost = self.trade_cost(gtp)
        return base_score - trade_cost

Real systems will:

separate this logic into services,
persist GTPs and clearing decisions in [MEM],
and integrate [ETH] and [EVAL] checks as first-class calls.

9. Safety, ethics, and “no dark markets”

Introduce “markets” and you immediately worry about:

collusion,
exploitation,
trading away non-negotiable rights.

SI-Core keeps a hard line:

Hard floors are non-tradeable
- Safety floors (e.g., max flood risk) cannot be traded.
- Ethics floors (e.g., fairness constraints) cannot become “bargain chips”.
All trades are logged structurally
- GTPs are first-class objects in [MEM].
- EthicsTrace includes “trade context”.
Multi-stakeholder ethics still applies
- viewpoint_base may list multiple stakeholder viewpoints.
- “Markets” must not systematically disadvantage a protected group.
PLB-M is auditable
- PLB-M proposals can themselves be audited.
- You can ask: “show me how PLB-M has changed Flood/Traffic deals over the last year.”

The “semantic economy” is:

an accounting and negotiation layer under strict governance, not a free-for-all trading system.

9.1 Adversarial and failure scenarios (non-normative)

Introducing negotiation and semantic pricing raises obvious questions about misuse and manipulation. A non-exhaustive threat model includes:

False reporting of impacts

An agent may misreport its ΔGCS to gain more favorable trades.

Mitigations:
- periodic ex-post audits comparing reported vs realized GCS,
- penalties (reduced priority, stricter oversight) for agents whose reports systematically diverge,
- trust scores or confidence weights on agents’ self-reports.
Collusion

A subset of agents may collude to disadvantage others (e.g., repeatedly trading in ways that push costs onto a third agent).

Mitigations:
- PLB-M can look for suspicious patterns (e.g., same pairs always benefiting at another’s expense),
- fairness constraints in [ETH] that limit how often any one agent or stakeholder can be systematically disadvantaged,
- policy rules limiting which agents can enter into trades together.
Denial-of-service via proposals

A malicious or malfunctioning agent may flood the system with GTPs.

Mitigations:
- rate limiting on trade proposals,
- minimum “proposal cost” for initiating GTPs,
- priority queues where high-trust agents or safety-critical trades are evaluated first.
Free-riding on semantic information

An agent consumes semantic units but rarely provides useful ones.

Mitigations:
- reciprocity rules (consumption proportional to contribution),
- caps on semantic bandwidth for low-contribution agents,
- contribution scores tracked in [MEM] and visible in governance.
Market manipulation

An agent may try to distort semantic prices or quotas for its own benefit.

Mitigations:
- bands on allowed price changes per time window,
- limits on rate-of-change of quotas,
- anomaly detection for sudden shifts not justified by external events.

All of these defenses are in addition to baseline SI-Core protections:

[ETH] overlays to enforce fairness and non-discrimination,
[MEM] for complete trade and price histories,
[EVAL] for gating high-risk changes.

PLB-M itself should be treated as untrusted but powerful: its proposals are subject to the same governance, audit, and rollback mechanisms as any human-designed change.

10. What PLB-M can learn over time

Given enough history, PLB-M can start to learn:

Stable patterns of cooperation
- which agent pairs negotiate frequently,
- which standard trades work well (few rollbacks, high GCS gains).
Bottlenecks and asymmetries
- which agents always “pay” in GCS,
- which semantic streams are persistently under-supplied.
Better contract templates
- refine GTP schemas to be simpler and safer,
- propose new, more robust standard deals.
Semantic price curves
- estimate how the marginal value of certain semantic units changes with load, risk, and context.

As always:

proposals are structural,
validated in sandbox,
subject to ethics and human oversight.

Over time, you get something like:

A learning market of meaning, where the “currency” is not money but structured contributions to shared goals.

10.1 Convergence and stability (non-normative)

Once PLB-M starts proposing changes to trade patterns, quotas, or semantic prices, the combined system becomes a feedback loop:

agents adapt to new trade rules,
PLB-M observes the new behavior,
PLB-M proposes another adjustment, and so on.

Without care, this can lead to:

oscillating trade patterns,
“price spirals” in semantic bandwidth,
over-correction, and
escalation of competition between agents.

We do not provide formal convergence proofs here, but we outline a few stability strategies:

Damping factors

Apply a damping factor to PLB-M’s proposals (e.g., “only move 10–20% of the way towards the suggested new parameter”), to avoid abrupt shifts.
Observation windows

Require that a pattern be stable over a meaningful window (e.g., weeks of data) before PLB-M is allowed to propose structural changes, to distinguish temporary fluctuations from true regularities.
Rollback of trades and rules

Treat major changes to trade rules or semantic prices as reversible actions:
- if instability is detected, roll back to the previous stable regime via RML-2/3 mechanisms,
- log both the forward and rollback steps in [MEM].
Circuit breakers

Define thresholds on:
- variance of trade outcomes,
- rate of semantic price change,
- per-agent “stress” metrics.
If thresholds are exceeded, pause PLB-M’s proposals, freeze current rules, and escalate to human review.
Conservative defaults

When PLB-M’s confidence is low (e.g., sparse data, conflicting patterns), bias towards reverting to known stable patterns rather than exploring aggressive new ones.

Formally, one might eventually analyze such systems using tools like:

Lyapunov-style stability arguments,
contraction mappings in policy space,
or regret minimization in repeated games.

Those are research directions, not assumptions of the current design. Operationally, you should treat PLB-M as an adaptive but constrained process whose effects are:

damped,
monitored for instability, and
roll-backable when needed.

11. Where this lives in the SI-Core / SI-NOS stack

In the existing SI picture:

L1/L2: single-agent safety and rollback,
L3: multi-agent consensus, partitions, dissent,
PLB: single-agent learning from failure.

This document proposes a non-normative extension:

Multi-agent goal layer:
- per-agent goals and GCS,
- joint action clearing,
- trade/contract representation (GTPs).
Semantic economy layer:
- semantic value estimation,
- priority and quota rules for semantic units.
PLB-M:
- pattern mining on joint behavior,
- proposal of new trade patterns,
- tuning of semantic pricing and negotiation policies.

Everything still sits under:

[OBS] — observation preconditions,
[ETH] — ethics overlays,
[MEM] — append-only ledgers,
[ID] — identity and origin,
[EVAL] — high-risk gating,
RML-2/3 — rollback for effectful changes.

12. Summary

Multi-agent SI systems don’t just optimize; they must co-exist.

This document sketched how to:

treat GCS as multi-agent accounting, not just single-agent reward;
model goal-space trades as structured, auditable “plea bargains” rather than ad-hoc compromises;
put prices on semantics in a way that respects goals, risk, and fairness;
extend PLB into PLB-M — a layer that learns market-like patterns of cooperation and information flow;
keep everything under SI-Core’s structural governance, avoiding “dark markets” of unaccountable trade.

The core idea:

If intelligence is structured movement across meaning layers, multi-agent intelligence is structured negotiation of those movements.

GCS and PLB gave us per-agent structure. Multi-agent goal trades and semantic pricing start to give us ecosystem-level structure.

This is not a finished design. It is:

a direction for how SI-Core can scale from “one careful core” to many interlocking, negotiating cores,
without giving up on traceability, ethics, and reversibility.

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